manifold-learning

🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps

PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) is a tool for visualizing high dimensional data.

CellRank: dynamics from multi-view single-cell data

Pytorch implementation of Hyperspherical Variational Auto-Encoders

Tensorflow implementation of Hyperspherical Variational Auto-Encoders

TorchDR - PyTorch Dimensionality Reduction

TLDR is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses

[𝗜𝗖𝗠𝗟 𝟮𝟬𝟮𝟲] Dispersion loss counteracts embedding condensation and improves generalization in small language models

Systematically learn and evaluate the latent geometry of high-dimensional data, with a focus on scRNAseq analysis

Pure MLX implementations of UMAP, t-SNE, PaCMAP, TriMap, DREAMS, CNE, MMAE, and NNDescent for Apple Silicon. Metal GPU for computation and video rendering.

Tensorflow implementation of adversarial auto-encoder for MNIST

This is the code implementation for the GMML algorithm.

Code and reuslts accompanying the NeurIPS 2022 paper with the title SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG

Code for the NeurIPS'19 paper "Guided Similarity Separation for Image Retrieval"

Dimensionality Reduction with Eilenberg-MacLane Coordinates

SPDlearn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization

Pytorch code for “Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment ” (DRMEA) (AAAI 2020).

Extended Dynamic Mode Decomposition for system identification from time series data (with dictionary learning, control and streaming options). Diffusion Maps to extract geometric description from data.

A flow matching model that uses data-driven geometric noise to improve the quality-generalisation tradeoff and to reduce memorisation.